MedMO: Grounding and Understanding
Multimodal LLMs for Medical Images

Multimodal large language models (MLLMs) have rapidly advanced, yet their adoption in medicine remains limited by gaps in domain coverage, modality alignment, and grounded reasoning. In this work, we introduce MedMO, a medical foundation model built upon a generalized MLLM architecture and trained exclusively on large-scale, domain-specific data.

MedMO follows a multi-stage training recipe: (i) cross-modal pretraining to align heterogeneous visual encoders with a medical language backbone; (ii) instruction tuning on multi-task supervision that spans captioning, VQA, report generation, retrieval, and grounded disease localization with bounding boxes; and (iii) reinforcement learning with verifiable rewards that combine factuality checks with a box-level GIoU reward to strengthen spatial grounding and step-by-step reasoning in complex clinical scenarios.

MedMO consistently outperforms strong open-source medical MLLMs across multiple modalities and tasks, achieving state-of-the-art performance in medical VQA, report generation, and diagnostic reasoning. Evaluations across radiology, ophthalmology, pathology, and emergency care confirm MedMO's broad cross-modality generalization and reliable spatial reasoning.